Statistical Learning for Resting-State fMRI: Successes and Challenges

Abstract : In the absence of external stimuli, fluctuations in cerebral activity can be used to reveal intrinsic structures. Well-conditioned probabilistic models of this so-called resting-state activity are needed to support neuroscientific hypotheses. Exploring two specific descriptions of resting-state fMRI, namely spatial analysis and connectivity graphs, we discuss the progress brought by statistical learning techniques, but also the neuroscientific picture that they paint, and possible modeling pitfalls.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [20 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00753132
Contributor : Gaël Varoquaux <>
Submitted on : Saturday, November 17, 2012 - 5:00:53 PM
Last modification on : Thursday, March 7, 2019 - 3:34:14 PM
Document(s) archivé(s) le : Monday, February 18, 2013 - 3:42:24 AM

Files

paper.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Gaël Varoquaux, Bertrand Thirion. Statistical Learning for Resting-State fMRI: Successes and Challenges. Machine Learning and Interpretation in Neuroimaging, NIPS workshop, Dec 2011, Sierra Nevada, Spain. pp.172-177, ⟨10.1007/978-3-642-34713-9_22⟩. ⟨hal-00753132⟩

Share

Metrics

Record views

528

Files downloads

575